What is Marker?
Marker is a deep learning PDF-to-Markdown converter designed for AI pipelines. It accurately extracts text, tables, equations, code blocks, and images from PDFs — including scanned documents. Unlike rule-based tools, Marker uses trained models for layout detection, OCR, table recognition, and equation conversion, achieving significantly higher accuracy on complex academic and technical documents.
Answer-Ready: Marker converts PDFs to clean Markdown using deep learning. Handles tables, equations, code blocks, multi-column layouts, and scanned documents. 10x faster than similar tools, 90%+ accuracy on academic papers. Used in RAG pipelines for document ingestion. 19k+ GitHub stars.
Best for: AI teams building RAG pipelines or processing technical PDFs. Works with: Any LLM framework, LangChain, LlamaIndex. Setup time: Under 3 minutes.
Core Features
1. High-Accuracy Extraction
| Element | Accuracy |
|---|---|
| Body text | 95%+ |
| Tables | 90%+ |
| Equations (LaTeX) | 85%+ |
| Code blocks | 90%+ |
| Multi-column | 90%+ |
2. Batch Processing
# Process 1000 PDFs with 8 workers
marker input_dir/ --workers 8 --output_format markdown3. Multiple Output Formats
# Markdown (default)
marker_single paper.pdf out/ --output_format markdown
# JSON (structured)
marker_single paper.pdf out/ --output_format json
# HTML
marker_single paper.pdf out/ --output_format html4. Language Support
Supports 50+ languages with automatic detection. Works especially well on English, Chinese, Japanese, Korean, and European languages.
5. GPU Acceleration
# Auto-detects CUDA/MPS
# CPU fallback available but slower
TORCH_DEVICE=cuda marker_single paper.pdf out/Marker vs Alternatives
| Feature | Marker | PyMuPDF | Zerox | Docling |
|---|---|---|---|---|
| Tables | Deep learning | Rule-based | Vision LLM | Deep learning |
| Equations | LaTeX output | Text only | Depends on LLM | Limited |
| Scanned PDFs | Built-in OCR | No | Yes (via LLM) | Yes |
| Speed | Fast (GPU) | Very fast | Slow (API calls) | Moderate |
| Cost | Free (local) | Free | API costs | Free |
| Accuracy | Very high | Moderate | High | High |
FAQ
Q: How does it compare to Zerox? A: Marker runs locally with no API costs and is much faster for batch processing. Zerox uses vision LLMs (GPT-4o) which cost per page but can handle edge cases better.
Q: Does it work on scanned PDFs? A: Yes, includes built-in OCR using deep learning models.
Q: What hardware do I need? A: GPU recommended for speed (NVIDIA CUDA or Apple MPS). CPU works but is 5-10x slower.